fMRI MODELS OF DENDRITIC AND ASTROCYTIC NETWORKS

Modeling the BOLD correlates of competitive neural dynamics

Winner-take-all models are commonly used to model decision-making tasks where one outcome must be selected from several competing options. Related random walk and diffusion models have been used to explain such processes and apply them to psychometric and neurophysiological data. Recent model-based fMRI studies have sought to find the neural correlates of decision-making processes. However, due to the fact that hemodynamic responses likely reflect synaptic rather than spiking activity, the expected BOLD signature of winner-take-all circuits is not clear. A powerful way to integrate data from neurophysiology and brain imaging is by developing biologically plausible neural network models constrained and testable by neural and behavioral data, and then using Synthetic Brain Imaging - transforming the output of simulations with the model to make predictions testable against neuroimaging data. We developed a biologically realistic spiking winner-take-all model comprised of coupled excitatory and inhibitory neural populations. We varied the difficulty of a decision-making task by adjusting the contrast, or relative strength of inputs representing two response options. Synthetic brain imaging was used to estimate the BOLD response of the model and analyze its peak as a function of input contrast. We performed a parameter space analysis to determine values for which the model performs the task accurately, and given accurate performance, the distribution of the input contrast-BOLD response relationship. This underscores the need for models grounded in neurophysiological data for brain imaging analyses which attempt to localize the neural correlates of cognitive processes based on predicted BOLD responses.

EEG/fMRI fusion based on independent component analysis: integration of data-driven and model-driven methods

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) provide complementary noninvasive information of brain activity, and EEG/fMRI fusion can achieve higher spatiotemporal resolution than each modality separately. This focuses on independent component analysis (ICA)-based EEG/fMRI fusion. In order to appreciate the issues, we first describe the potential and limitations of the developed fusion approaches: fMRI-constrained EEG imaging, EEG-informed fMRI analysis, and symmetric fusion. We then outline some newly developed hybrid fusion techniques using ICA and the combination of data-/model-driven methods, with special mention of the spatiotemporal EEG/fMRI fusion (STEFF). Finally, we discuss the current trend in methodological development and the existing limitations for extrapolating neural dynamics.

A natural science approach to consciousness

We begin with premises about natural science, its fundamental protocols and its limitations. With those in mind, we construct alternative descriptive models of consciousness, each comprising a synthesis of recent literature in cognitive science. Presuming that consciousness arose through natural selection, we eliminate the subset of alternatives that lack selectable physical phenotypes, leaving the subset with limited free will (mostly in the form of free won't). We argue that membership in this subset implies a two-way exchange of energy between the conscious mental realm and the physical realm of the brain. We propose an analogy between the mental and physical phases of energy and the phases (e.g., gas/liquid) of matter, and a possible realization in the form of a generic resonator. As candidate undergirdings of such a system, we propose astroglial-pyramidal cell and electromagnetic-field models. Finally, we consider the problem of identification of the presence of consciousness in other beings or in machines.

QUANTUM MECHANICS AND BRAIN UNCERTAINTY

This paper argues that molecular governing structures (such as receptors, gating molecules, or ionic channels) which operate pervasively in the brain, often with small number particle systems (as, for example, at the surfaces of membranes, synaptic clefts, or macromolecules), may plausibly be vehicles for the transmutation of quantum mechanical fluctuations to normal-level neural signaling.